Today I watched the video made by Andrew Ng about the Supervised Learning and Unsupervised Learning, I want to write a summary to tell what is Supervised Learning and what is Unsupervised Learning.

Supervised Learning

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems. Regression means predicting the output. Classification means we are trying map input variables into discrete categories

Eg: Housing price prediction

Breast cancer(malignant,begin)

Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

In unsupervised learning, we’re given data that looks different than data that looks like this that doesn’t have any labels or that all has the same label or really no labels.

Clustering algorithm problem is the symbol of Unsupervised Learning.

Eg: Cocktail party problem algorithm

End

In short, in Supervised Learning, we know clear expected output. But in Unsupervised Learning, there is no expectation of the output.